Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.

中文翻译：

---

使用有符号距离场的度量单目定位

度量定位在基于视觉的导航中起着至关重要的作用。为了克服外观变化下匹配光度的退化，最近的研究求助于引入先验场景结构的几何约束。在本文中，我们提出了一种用于单目相机的度量定位方法，使用有符号距离场 (SDF) 作为全局地图表示。利用来自 SDF 的体积距离信息，我们的目标是放松对先前方法中局部束调整 (BA) 的精确结构的假设。通过将距离因子与时间视觉约束紧密耦合，我们的系统可以纠正里程计漂移并联合优化全局相机姿势与局部结构。我们在室内和室外公共数据集上验证了所提出的方法。